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研究生(外文):Chen, Zhi-Ying
論文名稱(外文):The remote control robotic arm with mobile application to barcode identification and inventory of goods
指導教授(外文):Chen, Chin-Tai
口試委員(外文):Chen, Chin-TaiKang, Yaw-HongSong, Kai-Tai
外文關鍵詞:Robotics armBarcode identificationGripperClamping force
  • 被引用被引用:1
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今日各種終端零售商林立,每一間商店販賣的東西越來越多,隨著消費者不斷的進出商店,使得商品流通快速,使用人力的方式進行商品管理面臨到的挑戰越來越多。倘若能將自動化概念導入現今終端零售商可望解決商店人員無法及時得知商品缺貨與錯置之問題。因此本研究自製6軸機械手臂,並結合現今流行嵌入式電腦-Raspberry Pi,其能搭配行動裝置應用程式(APP)以藍芽無線遠端控制的方式,進行機器手臂的操作。並且透過電腦視覺辨識來執行貨品條碼辨識與盤點。本研究所設計的系統可以辨識常見於商店中的一維條碼(EAN碼)。此外,我們設計一款應用程式,能藉由手持行動裝置進行機器手臂狀態查看與操作;本機器手臂之夾爪具備力量感測功能以便即時調整力量。其夾取物件時,可依照不同物件的材質與軟硬度,給予不同的夾持力,以避免物件變形或受損。整體系統能在嵌入式電腦上執行,解決以往使用一般個人電腦體積、重量與耗電量的問題。最後,進行此機器手臂移動測試與夾爪夾持力測試。由實驗結果中得知,機器手臂三軸座標平均誤差X軸為2.51 %、Y軸為0.15 %與Z軸為0.28 %;在夾取物件後X軸平均誤差上升0.05 %、Y軸上升0.01 %,Z軸則維持不變。夾爪本身能夾取的最大重量為250 gw。但安裝於機器手臂上,最大只能抓取70 gw的物品。
Nowadays, a variety of terminal retailers are around us everywhere. Every store is selling more and more goods, as consumers enjoy shopping at stores. With the rapid flow of goods, manual management and control of merchandise is confronted with more challenges. If automation can be applied for the retailers, it is expected to solve the manual problems such as goods out of stock and being misplaced that staffs are usually unware of. In this study, we made a 6-axis robotic arm embedded with a tiny computer Raspberry Pi, which is widely used for remote control robotic arm via Bluetooth to operate the robot arm. And we used computer vision for barcode identification and inventory of goods. The system designed in this study can identify the common one-dimensional bar codes (EAN code) of goods in stores. In addition, we designed a mobile application (APP) program, which can monitor and operate the robotic arm via the mobile devices. The gripper of this robotic arm had a function of force sensing for real-time control of clamping force. With the goods packaged by different material and rigidity, the gripper can be set to give different clamping force, in order to avoid damage or deformation of them. Finally, through proper mechanical design, we made the force of the gripper uniform applied to the sensor, in order to avert the problem about inaccurate measurement of the force. The overall system can be implemented in an embedded computer to avoid using a personal computer with large volume, weight, and power consumption. Finally, some tests were performed for robotic arm movement and clamping force of gripper. From the experimental results of motion, we found that the average errors of X, Y, and Z-axis were 2.51 %, 0.15 %, and 0.28 %, respectively. With gripping, the average errors increased by 0.05 % in X-axis, 0.01 % in Y-axis, but remained the same in Z-axis. The gripper picked up a maximum weight of 250 gw, while a maximum of 70 gw was affordable as it being installed on the arm.
中文摘要 i
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第1章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
第2章 文獻回顧 3
2.1 貨品管理 3
2.2 機器手臂 5
2.3 機器手臂夾爪 7
2.4 視覺辨識 10
2.5 結論 20
第3章 控制方法及機器手臂設計 23
3.1 整體零件 23
3.2 機器手臂設計 25
3.3 夾爪設計 36
3.3.1 力量感測器 40
3.4 運動分析 41
3.4.1 順向運動學 43
3.4.2 逆向運動學 47
3.5 條碼 53
3.5.1 條碼類別 53
3.5.2 條碼結構 54 UPC碼 55 EAN碼 57
3.5.3 條碼編碼 58 條碼編碼 58 條碼檢查碼 61
3.5.4 條碼尺寸 61
3.5.5 條碼讀取方式 62
3.6 視覺辨識 62
3.6.1 二值化 67
3.6.2 形狀辨識 69
3.6.3 SURF特徵演算法 70
3.7 控制元件與系統設計 71
3.7.1 Arduino Nano單晶片控制板 71
3.7.2 Raspberry Pi嵌入式電腦 72
3.7.3 10吋觸控式螢幕 73
3.7.4 系統設計 73
3.8 實驗環境設計 75
3.8.1 力量感測器線性度量測 75
3.8.2 夾爪夾持力測試 76
3.8.3 不同顏色條碼測試 77
3.8.4 機器手臂貨品盤點測試 78
第4章 結果與討論 79
4.1 機器手臂與夾爪組裝 79
4.2 機器手臂控制 85
4.2.1 機器手臂各軸角度範圍 85
4.2.2 機器手臂姿態模擬 90
4.2.3 機器手臂正逆向運動學驗證 92
4.2.4 機器手臂工作區域 102
4.3 夾持力測試 103
4.3.1 力量感測器壓力-電阻(電導) 103
4.3.2 夾爪夾持力測試 104
4.4 視覺辨識 105
4.4.1 條碼辨識 106
4.4.2 商品特徵比對 110
4.5 使用者圖形化介面 112
4.6 行動裝置App 115
4.7 整合嵌入式電腦 117
4.8 機器手臂貨品盤點測試 119
第5章 結論與未來展望 124
5.1 結論 124
5.2 未來展望 127
參考文獻 128
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